Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
Write clear, engaging technical content from real experience. Use when writing blog posts, documentation, tutorials, or technical articles.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<default_to_action> When writing technical content:
Blog Post Structure:
# Title (specific promise)
## Opening (2-3 paragraphs)
- Hook: The problem or insight
- Context: Why this matters
- Promise: What they'll learn
## Body (3-5 sections)
- One clear idea per section
- Support with examples/code/data
## Closing
- Key takeaway (1-2 sentences)
- Action reader can take
Before/After: ❌ "We implemented a comprehensive testing strategy..." ✅ "We moved exploratory testing into sprint planning. QE now pairs with devs during story refinement." </default_to_action>
| Principle | Bad | Good |
|---|---|---|
| Lead with value | "In today's landscape..." | "Here's how we cut bugs 60%" |
| Show, don't tell | "We improved testing" | "Bug detection: 12→47 per sprint" |
| Be specific | "Performance improved" | "Response time: 2.3s→180ms" |
| Honest trade-offs | "This approach is best" | "TDD slowed velocity 20%, reduced bugs 75%" |
| Kill | Reason |
|---|---|
| basically, actually, probably | Hedge words |
| leverage, synergy, paradigm | Corporate speak |
| very, really, quite | Unnecessary qualifiers |
| it should be noted that | Just note it |
Before publishing:
Before: "We decided to implement a more comprehensive testing strategy that would allow us to catch bugs earlier in the development lifecycle."
After: "We moved exploratory testing into sprint planning. QE now pairs with devs during story refinement, identifying risks before code is written."
Before: "The benefits of this approach are numerous and include improved quality, faster feedback loops, and better team collaboration."
After: "Three outcomes: bugs found 2 days earlier on average, 30% fewer regression issues, and devs now ask QE for input during design."
// Generate documentation from code
const docs = await Task("Generate Docs", {
source: 'src/services/PaymentService.ts',
format: 'markdown',
includeExamples: true
}, "qe-quality-analyzer");
// Review documentation quality
const review = await Task("Review Docs", {
files: ['README.md', 'docs/api.md'],
checkClarity: true,
checkCodeExamples: true
}, "qe-quality-analyzer");
aqe/technical-writing/
├── generated-docs/* - Auto-generated documentation
├── reviews/* - Documentation review findings
└── templates/* - Reusable doc templates
const docsFleet = await FleetManager.coordinate({
strategy: 'documentation',
agents: [
'qe-quality-analyzer', // Generate and review
'qe-api-contract-validator' // API doc accuracy
],
topology: 'sequential'
});
You're not writing to impress. You're writing to help people solve problems you've already solved. Be the colleague you wish you'd had.
Write from experience. Only write about what you've done in production. If exploring, say so.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
中文简介正在准备中。当前显示原文说明。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
中文简介正在准备中。当前显示原文说明。